Abstract
Social emotion mining and its cause identification are important tasks in Web-based social media analysis and text mining with many domain applications, which focus on analyzing the emotions evoked to the reader and their corresponding causes. Previously, they are conducted as separate tasks. Based on the cognitive emotion theory, there is a deep coupling relationship between social emotion and its cause identification. In this paper, we propose a cognitive knowledge enriched joint framework (JointPSEC) for predicting social emotion and its causes. Specifically, we formulate the rules based on the cognitive emotion model and utilize this knowledge together with the lexicon-based knowledge to improve emotion-clause relation learning for social emotion mining. Meanwhile, we utilize the predicted emotion to enhance dimension-clause relation learning for cause identification. Our method is mediated by cognitive knowledge to mutually facilitate the joint prediction task. Experimental results on the benchmark dataset verify the effectiveness of our framework.
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Acknowledgements
This work is supported by the Ministry of Science and Technology of China under Grant #2022YFB2703302, NNSFC under Grants #11832001 and #62206287, and Beijing Nova Program Z201100006820085 from Beijing Municipal Science and Technology Commission.
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Xiao, X., Tian, Y., Luo, Y., Mao, W. (2023). A Cognitive Knowledge Enriched Joint Framework for Social Emotion and Cause Mining. In: Jin, Z., Jiang, Y., Buchmann, R.A., Bi, Y., Ghiran, AM., Ma, W. (eds) Knowledge Science, Engineering and Management. KSEM 2023. Lecture Notes in Computer Science(), vol 14118. Springer, Cham. https://doi.org/10.1007/978-3-031-40286-9_32
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DOI: https://doi.org/10.1007/978-3-031-40286-9_32
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